Sammon Quadratic Recurrent Multilayer Deep Classifier for Legal Document Analytics

In recent years, machine learning algorithms and in particular deep learning has shown promising results when used in the field of legal domain. The legal field is strongly affected by the problem of information overload, due to the large amount of legal material stored in textual form. Legal text processing is essential in the legal domain to analyze the texts of the court events to automatically predict smart decisions. With an increasing number of digitally available documents, legal text processing is essential to analyze documents which helps to automate various legal domain tasks. Legal document classification is a valuable tool in legal services for enhancing the quality and efficiency of legal document review. In this paper, we propose Sammon Keyword Mapping-based Quadratic Discriminant Recurrent Multilayer Perceptive Deep Neural Classifier (SKM-QDRMPDNC), a system that applies deep neural methods to the problem of legal document classification. The SKM-QDRMPDNC technique consists of many layers to perform the keyword extraction and classification. First, the set of legal documents are collected from the dataset. Then the keyword extraction is performed using Sammon Mapping technique based on the distance measure. With the extracted features, Quadratic Discriminant analysis is applied to perform the document classification based on the likelihood ratio test. Finally, the classified legal documents are obtained at the output layer. This process is repeated until minimum error is attained. The experimental assessment is carried out using various performance metrics such as accuracy, precision, recall, F-measure, and computational time based on several legal documents collected from the dataset. The observed results validated that the proposed SKM-QDRMPDNC technique provides improved performance in terms of achieving higher accuracy, precision, recall, and F-measure with minimum computation time when compared to existing methods.